Any diagnostic test should be evaluated for certain criteria. It should be possible to define the sensitivity, specificity, positive predictive value and negative predictive value. All of these criteria are needed to understand the usefulness of any test.

Given suitable data it should be possible to calculate sensitivity and specificity. If the sensitivity, specificity and prevalence of a condition are known it should be possible to calculate positive and negative predictive values.

It is necessary to understand these parameters of a test to decide the clinical applicability and usefulness.

True result

positive

negative

Test result

positive

True Positive

False Positive

Sensitivity= TP/(TP+FN)

negative

False Negative

True Negative

Sensitivity= TP/(TP+FN)

The sensitivity is the proportion with the abnormality which the test classifies as abnormal. This is the proportion of true positives.

The specificity is the proportion of normals which the test calls normal. This is the proportion of true negatives.

The positive predictive value is the proportion of cases with a positive test result which are truly positive. (TP/TP+FP)

A valid test should have both a high sensitivity and specificity. It is usual for the specificity to decrease as the sensitivity increases and it is usually necessary to find a balance where the test is truly clinically useful.

Many tests are used for screening. These are tests are those that are used in the absence of a specific complaint being investigated. A screening test should meet certain prerequisites.

1.A preclinical condition can be reliably detected.
2.A clinical outcome can be affected by the knowledge.

It is very important to understand the sensitivity, specificity, and predictive value of any screening test in order for it to be used reliably.

There are two types of epidemiological (what causes something) study: experimental and observational studies.

In experimental studies ( clinical trials, intervention studies, randomized controlled trials) the investigator controls which treatment is given, usually by some system of random assignment. Where harmful effects are expected (cigarette smoking) it is not ethical to follow strict randomization so it is usual to observe the results of natural treatment assignment in an observational study.

In observational studies the intent is to observe how exposure to risk factors influences the likelihood of developing disease. Observational studies can be subject to selection bias. Random assignment in controlled trials is designed to overcome selection bias.

There are three types of observational study; cross-sectional (prevalence), cohort (longitudinal), and case-control.

These are used to estimate the prevalence of diseases or the prevalence of exposure to risk factors or both. Certain steps have to be completed in these studies.

1. define the target population. This can be age, sex, place of residence,
occupation, exposure to a named substance, etc.
2. take a random sample of this population
3. determine a case-definition by defining fixed criteria by which the condition
being studied is identified.
4. complete the case-ascertainment by applying the case definition to the
sample.
5. analysis of the data to estimate the prevalence rate.

These studies are widely used. Evaluation of the prevalence of a condition can be important in health care planning. Prevalence is not just related to risk factor exposure but is also influenced by disease duration. This brings up the need for cohort studies which estimate the incidence of a condition.

1. Define a hypothesis to be tested.
2. Define the population to be studied.
3. Take a sample of this population
4. Evaluate the exposure status by applying a test to the sample to determine the
presence or absence of a risk factor in each individual.
5. Exclude cases from the studies who already have the disease.
6. Continue to observe the sample in a follow up being careful to follow the
cases closely to maintain a high response rate.
7. Monitor the cases for their outcome; detect cases of disease or death.

Cohort studies or longitudinal studies involve the follow-up of individual cases. A cohort study can be:

prospective: Individuals who are exposed to a risk factor are followed for a
defined length of time and the effects of the risk factor on the exposed group
is compared to a group that was not exposed.historical: A cohort is identified for whom records of exposure exist and for
whom disease experience can be measured after a substantial period of time.
The exposure is an assigned risk factor which may be a genetic trait, an environmental exposure, a lifestyle or specific physiologic risk factors.

Cohort studies are expensive because for rare diseases a large sample is needed.

Case control studies can be more efficient for rare diseases but are more easily subject to bias.

A case control study follows well defined steps.

1. Identify cases of the disease to be studied. Ideally all the cases in a population
should be studied to lessen selection bias. New (incident) cases should be
selected because previously diagnosed (prevalent) cases represent long term
survivors.
2. Recruit control subjects without disease from the same population. At least one
control is needed for each case studied but two or more controls can be used to
increase the statistical power. The controls should be matched as to age, sex
etc. to the cases being studied.
3. Test cases and controls for prior exposure to the risk factor being evaluated.
4. Analyze the results. In case control studies the rate of the disease in the
population is unknown.

The study must be careful to evaluate associations and causes. The following are some criteria to decide whether an association is causal.

1. Strength. A strong association is likely to be causal.
2. Consistency. The observation is repeatedly seen in different persons,
at different places, at different times.
3. Temporality. The cause comes before the effect on the time line.
4. Dose response relationship. Increased exposure relates to increased risk
of the condition being studied.
5. Biologic plausibility.The possible causative mechanism fits in with accepted
biologic knowledge.
6. Coherence. Fits in with other evidence of trends in other factors associated.
7. Anology. Similar to cause and effect established for equivalent exposure and disease.
8. Specificity. One cause linked to one effect.
9. Experiment. Removing the cause removes the effect.